GENERATIVE ADVERSARIAL NETWORKS AS CREATIVE PARTNERS IN DIGITAL PAINTING AND ILLUSTRATION WORKFLOWS

Vijaya Balpande; Neha Rana; Rajesh Raikwar; Baoxin Le; Atish Baburao Mane; Asha Rani G

doi:10.29121/shodhkosh.v7.i4s.2026.7468

Authors

Vijaya Balpande Department of Computer Science and Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, India
Neha Rana School of Pharmacy, Noida International University, Greater Noida, Uttar Pradesh 203201, India
Rajesh Raikwar Assistant Professor, Department of Electrical Engg., Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
Baoxin Le Faculty of Education, Shinawatra University
Atish Baburao Mane Department of Mechanical Engineering, Bharati Vidyapeeth's College of Engineering, Lavale, Pune, Maharashtra, India
Asha Rani G Assistant Professor, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7468

Keywords:

Generative Adversarial Networks, Digital Art, Artificial Intelligence in Creativity, Digital Painting, Illustration Workflows, Human–AI Collaboration, Computational Creativity

Abstract [English]

The application of artificial intelligence to creative processes has profoundly changed the modern digital art and illustration processes. Generative Adversarial Networks (GANs) are among many other AI approaches that have become potent in creating quality visual art and assisting the exploration of art. In this paper, the author explores the use of GANs as a creative collaborator in the digital painting and illustration workflow. The paper analyzes the technical principles behind GAN architectures, their use in the artistic image generation, and their role in human-AI creative processes. The most important applications of GAN systems, such as concept generation, style transfer, image-to-image translation, and automated colorization, are examined in order to comprehend how the technologies can support an artist at any phase of visual creation. The examples of major GAN models DCGAN, CycleGAN, StyleGAN, and StyleGAN2 are also compared to discuss the effectiveness of these models in the synthesis of artistic images. According to the results provided, it is seen that advanced architectures are better in image realism, consistency of structures and artistic usability than the previous models. Moreover, the study demonstrates the advantages of GAN-based tools as it promotes quick ideation, experimentation with styles, and design feedback, without taking control of the creative process of artists. The paper also talks of the technical structures of integrating GAN systems in the digital art setting and talks about issues of ethical issues surrounding authorship, originality, and bias in the dataset. All in all, the results indicate that the technologies based on GAN redefine the production of digital art by facilitating the interactive collaboration between human creativity and machine intelligence and creating new opportunities in the realm of innovations in computational creativity and digital illustration practice.

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